We propose to create new software and analysis methods designed to make possible the exploration of a unique dataset, the 1,004 genomes sequenced by the Consortium on Asthma among African-Ancestry Populations in the Americas (CAAPA). The size of this dataset, over 130 Terabytes, currently prevents it from being explored with alignment-based tools, and researchers instead are limited to using the much smaller files containing single-nucleotide variants. Our proposed software will make this dataset and others like it available for real- time searching, a capability that is not yet possible for any genomic database of this size. Since the early 1990s, scientists have used DNA sequence databases to study a wide range of problems, including novel gene discovery, mutation detection, the investigation of larger structural variants, and evolutionary processes. The ability to search all known genes and genomes using BLAST and similar programs has long been assumed, and sequence search engines throughout the world provide this ability. However, the vast size of the CAAPA dataset makes it impossible to search the data itself using current tools. One cannot look for specific mutations, extract and re-analyze data for any particular gene or regulatory region, or look for structural variants. Newer, fast next-generation sequence alignment programs such as Bowtie, originally developed in our group, allow far faster alignment of NGS reads to the genome, but even these programs cannot search data on the scale of CAAPA in real time. Different architectures need to be designed and built to accommodate these very large datasets. The CAAPA exploration system (CESYS) will use a combination of a highly efficient database, very fast storage, and fast search algorithms to achieve our goals. This project aims to accomplish several goals that will dramatically enhance the value of CAAPA. First, the data will be made available to a very large community of researchers, who can use it not only to study the genetics of asthma and allergy in the CAAPA populations, but also to compare these subjects to other groups. The data currently resides on hard drives and is available only to a small number of the project's PIs, a situation that limits its value. Second, b creating an authentication system consistent with dbGaP, we will create a data sharing model that other projects can use and that will remove some of the technical barriers to sharing genome data from human subjects. Third, as part of building the database, we will re-call all the SNPs using the newly released human genome build (hg20), creating a consistent set of variants that we will also share freely through the project database. Fourth, we will identify all bacterial contaminants, including those in a subset of subjects known to have bloodstream infections at the time of sample collection. Fifth, we will identify structural variants unique to he CAAPA population, which we can then explore for any association with the risk of asthma.